Presentation
Advanced APL Modeling Method for Complex I/O Buffer Designs for accurate SoC IR Drop Analysis
DescriptionThe paper "Innovative Approach for APL (Apache Power Library) Modeling for Complex IO Designs" presents a new methodology to improve IR drop analysis accuracy in complex IO designs. Traditional IR drop analysis in System-on-Chip (SoC) designs uses models like APL for standard cells, CMM/APL for macros, and NLPM .libs for IO cells. However, NLPM .libs are inadequate for complex IO cells, necessitating a novel APL modeling approach.
The challenges in APL modeling for IO cells stem from their complexity, making traditional methods like sim2iprof cumbersome. The proposed methodology uses a characterization engine to generate CCS-POWER .lib, simplifying the process and reducing generation time. The comprehensive APL views cover all Process, Voltage, and Temperature (PVT) conditions, arcs, and slope x load combinations, aligning with NLPM data.
Comparative analysis shows the APL-CCS method offers a simple setup, quick generation, and comprehensive data without additional simulations. Two analyses validate the method: one compares APL-CCS with sim2iprof and SPICE, and the other compares peak current values from NLPM and APL-CCS models against SPICE simulations. Results show APL-CCS and SPICE report peak currents 2X to 25X higher than NLPM.
The paper concludes that accurate SoC analysis for complex IO cells requires APL models for better accuracy than NLPM .libs. The APL-CCS approach simplifies data generation, making it accessible for industry adoption and enhancing IR drop analysis for complex IO designs.
The challenges in APL modeling for IO cells stem from their complexity, making traditional methods like sim2iprof cumbersome. The proposed methodology uses a characterization engine to generate CCS-POWER .lib, simplifying the process and reducing generation time. The comprehensive APL views cover all Process, Voltage, and Temperature (PVT) conditions, arcs, and slope x load combinations, aligning with NLPM data.
Comparative analysis shows the APL-CCS method offers a simple setup, quick generation, and comprehensive data without additional simulations. Two analyses validate the method: one compares APL-CCS with sim2iprof and SPICE, and the other compares peak current values from NLPM and APL-CCS models against SPICE simulations. Results show APL-CCS and SPICE report peak currents 2X to 25X higher than NLPM.
The paper concludes that accurate SoC analysis for complex IO cells requires APL models for better accuracy than NLPM .libs. The APL-CCS approach simplifies data generation, making it accessible for industry adoption and enhancing IR drop analysis for complex IO designs.
Event Type
Engineering Poster
Networking
TimeMonday, June 235:00pm - 6:00pm PDT
LocationEngineering Posters, Level 2 Exhibit Hall